(1. Faculty of Engineering，China University of Geosciences(Wuhan)，Wuhan，Hubei 430074，China；2. PowerChina Chengdu Engineering Corporation Limited，Chengdu，Sichuan 610072，China)
Abstract：It is a crucial work in the research of landslide deformation status and hazard assessment to predict the displacement of step-type landslides. Generally，the research of step prediction is stationary fluctuation signal. The deformation rate decomposition method based on the step motion characteristics of landslide provides a new idea of step landslide signal decomposition in nonlinear deformation stage. Taking Gapa landslide as an example，the deformation rate data is obtained by signal differential and smoothing method of discrete wavelet transform(DWT). Based on the step motion characteristics of landslide，the deformation rate data is decomposed into small-scale fluctuation items determined by external inducers and large-scale trend items determined by internal control factors，in which the deformation rate trend signal is predicted by Inverse logistic function model(ILF) with added oscillation function. The deformation state of landslide is judged by curvature extreme value method. Deformation rate fluctuation term signals were predicted by constructing nonlinear mapping models using long short term memory LSTM，with rainfall and water level as evoked inputs，and the prediction results of trend term as control inputs. The prediction results show that the decomposition model based on the rate of deformation is more accurate than the traditional displacement fitting decomposition model for the non-linear process data of the Gapa Landslide，and its ability to map external factors is stronger. Therefore，deformation rate decomposition is an effective idea for prediction based on step motion mechanism，which solve the problem of step landslide prediction in nonlinear deformation stage.
邢保印1，张炜怡1，章广成1，张世殊2，刘忠绪2，曾 鑫1，郑子涵1. 基于变形速率分解的阶跃型滑坡预测——以呷爬滑坡为例[J]. 岩石力学与工程学报, 2023, 42(3): 685-697.
XING Baoyin1，ZHANG Weiyi1，ZHANG Guangcheng1，ZHANG Shishu2，LIU Zhongxu2，ZENG Xin1，ZHENG Zihan1. Prediction of step-type landslides based on deformation rate decomposition—#br#
a case study of Gapa landslide. , 2023, 42(3): 685-697.
杨 丽，吴雨茜，王俊丽，等. 循环神经网络研究综述[J]. 计算机应用，2018，38(增2)：1-6.(YANG Li，WU Yuqian，WANG Junli，et al. Research on recurrent neural network[J]. Journal of Computer Applications，2018，38(Supp.2)：1-6.(in Chinese))
[7]
黄 健，李 桥，巨能攀，等. 基于主控因子分析与GM-IAGA-WNN联合模型的平推式滑坡位移预测研究——以垮梁子滑坡为例[J]. 工程地质学报，2019，27(4)：862-872.(HUANG Jian，LI Qiao，JU Nengpan，et al. Displacement prediction of translational landslide based on anslysis of major factors and gm-iaga-wnn model—a case study of Kualiangzi landslide[J]. Journal of Engineering Geology，2019，27(4)：862-872.(in Chinese))
[9]
LU X S，MIAO F S，XIE X X，et al. A new method for displacement prediction of “step-like” landslides based on VMD-FOA-SVR model[J]. Environmental Earth Sciences，2021，80(17)：542.
[2]
TANG H M，GONG W P，LI C D，et al. A new framework for characterizing landslide deformation：a case study of the Yu-Kai highway landslide in Guizhou，China[J]. Bulletin of Engineering Geology and the Environment，2018，78(6)：4 291-4 309.
[4]
周 超，殷坤龙，黄发明. 混沌序列WA-ELM耦合模型在滑坡位移预测中的应用[J]. 岩土力学，2015，36(9)：2 674-2 680.(ZHOU Chao，YIN Kunlong，HUANG Faming. Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction[J]. Rock and Soil Mechanics，2015，36(9)：2 674-2 680.(in Chinese))
[6]
YANG B B，YIN K L，LACASSE S，et al. Time series analysis and long short-term memory neural network to predict landslide displacement[J]. Springer Science and Business Media LLC，2019，16(4)：677-694.
[8]
DU J，YIN K L，LACASSE S. Displacement prediction in colluvial landslides，Three Gorges Reservoir，China[J]. Landslides，2012，10(2)：203-218.
[11]
李麟玮，吴益平，苗发盛，等. 考虑变形状态动态切换的阶跃型滑坡位移区间预测方法[J]. 岩石力学与工程学报，2019，38(11)：2 272-2 287.(LI Linwei，WU Yiping，MIAO Fasheng，et al. Displacement interval prediction method for step-like landslides considering deformation state dynamic switching[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(11)：2 272-2 287.(in Chinese))
[16]
张雪东，陈剑平，黄润秋，等. 用FLAC-3D分析呷爬滑坡的变形特征[J]. 岩土力学，2005，26(1)：131-134.(ZHANG Xuedong，CHEN Jianping，HUANG Runqiu，et al. A study of deformation features of Gapa landslide using FLAC-3D[J]. Rock and Soil Mechanics，2005，26(1)：131-134.(in Chinese))
[12]
许 强，曾裕平. 具有蠕变特点滑坡的加速度变化特征及临滑预警指标研究[J]. 岩石力学与工程学报，2009，28(6)：1 099-1 106.(XU Qiang，ZENG Yuping. Research on acceleration variation characteristics of creep landslide and early-warning prediction indicator of critical sliding[J]. Chinese Journal of Rock Mechanics and Engineering，2009，28(6)：1 099-1 106.(in Chinese))
[5]
ZHANG J R，TANG H M，TANNANT D D，et al. Combined forecasting model with CEEMD-LCSS reconstruction and the ABC-SVR method for landslide displacement prediction[J]. Engineering Geology，2014，293：126205.
[10]
李 聪，朱杰兵，汪 斌，等. 滑坡不同变形阶段演化规律与变形速率预警判据研究[J]. 岩石力学与工程学报，2016，35(7)：1 407-1 414.(LI Cong，ZHU Jiebing，WANG Bin，et al. Critical deformation velocity of landslides in different deformation phases[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(7)：1 407-1 414.(in Chinese))
[3]
HU X L，WU S S，ZHANG G C，et al. Landslide displacement prediction using kinematics-based random forests method：A case study in Jinping Reservoir Area，China[J]. Engineering Geology，2021，283：105975.
[15]
WU Shuangshuang，HU Xinli，ZHENG Wenbo，et al. Displacement behaviour and potential impulse waves of the Gapa landslide subjected to the Jinping Reservoir fluctuations in Southwest China[J]. Geomorphology，2021，397：108013.
[1]
张 俊，殷坤龙，王佳佳，等. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报，2015，34(2)：382-391.(ZHANG Jun，YIN Kunlong，WANG Jiajia，et al. Displacement prediction of baishuihe landslide based on time series and PSO-SVR model[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(2)：382-391.(in Chinese))
[13]
许 强，汤明高，徐开祥，等. 滑坡时空演化规律及预警预报研究[J]. 岩石力学与工程学报，2008，27(6)：1 104-1 112.(XU Qiang，TANG Minggao，XU Kaixiang，et al. Research on space-time evolution laws and early warning-prediction of landslides[J]. Chinese Journal of Rock Mechanics and Engineering，2008，27(6)：1 104-1 112.(in Chinese))
2023, Vol. 42 
